Berkeley Vision and Learning Center(BVLC)-Berkeley AI Research(BAIR)



Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. - Official website Caffe allows switching between CPU and GPU by setting a single flag.

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